Horizons Ventures has backed 3 unique companies on the map: Viv Labs, Sentient Technologies, and Affectiva.

Increased investor interest in AI startups – from around 10 deals in Q1’11 to over 120 in Q2’16 – can be attributed to recent advances in machine learning algorithms, particularly “deep learning” technology, a souped up version of AI. Just this week, Google integrated deep learning into its Google Translate tool; Baidu announced the launch of DeepBench, an “open source benchmarking tool for evaluating deep learning performance across different hardware platforms”; and NVIDIA introduced Xavier, a deep learning-based supercomputer for driverless cars.

In the private market, Google put deep learning in the spotlight back in 2014 when it acquired 4 startups focused on this AI tech in quick succession: DeepMind, Vision Factory, Dark Blue Labs, and DNNresearch. Apple, which joined the race in 2015, most recently acquired Turi, which has developed a deep learning toolkit, among other AI-based solutions. Not to be outdone, Intel has acquired around 5 AI startups since January 2015, including deep learning startup Nervana Systems and, more recently, Movidius.

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In the light of increased investor interest in deep learning and a wave of new startups applying this technology to verticals like healthcare, cybersecurity, and business intelligence, we used the CB Insights database to identify 60+ startups in the space. As recently as 2013, the space saw fewer than 10 deals. But since January 2015, deep learning startups have raised over 70 equity deals in aggregate, and over $600M in equity funding since 2012.

This market map is not meant to be exhaustive of companies in this space. Please click to enlarge.

Deep_learning_MarketMap_sept2016_2

Here’s a look at the categories we identified within deep learning:

Computer Vision: Startups here are using deep learning for image recognition, analytics, and classification. Aerial image analytics startup Terraloupe was seed-funded this year by Germany-based Bayern Kapital. New York-based Calrifai — backed by investors including Google Ventures, Lux Capital, and NVidia — entered the R/GA accelerator this year, after raising $10M in Series A in Q2’15. Captricity, which extracts information from hand-written data, has raised $49M in equity funding so far from investors including Social Capital, Accomplice, White Mountains Insurance Group, and New York Life Insurance Company.

Speech analytics/conversational interface: Google made news last year when it entered the Chinese market with its investment — a $60M Series C round — in Shanghai-based Mobvoi. The smart watch maker’s core tech includes speech recognition, text-to-speech conversion, and semantic analysis. Another startup that has recently been in the news is Viv Labs, which demonstrated its Siri-like AI assistant earlier this year. The company has raised $30M in equity funding from investors including Horizons Ventures and Pritzker Group Venture Capital.

Core AI: Startups here are developing algorithms that can be applied across multiple industries like finance, healthcare, and e-commerce. To name a few, Japan-based LeapMind raised $3.4M from Archetype Ventures, ITOCHU Technology Ventures, and Visionnaire Ventures in Q3’16; Teradeep, a neural network startup that “accelerates” deep learning via field programmable gate arrays (FPGAs) — integrated circuits that can be programmed for customer-specific applications — received funding in Q1’16 from the corporate venture arm of XILINX.

Auto & Robotics: 5 out of 6 startups in this category raised their first equity funding rounds this year. Machine vision company Netradyne, which has developed a driver-safety platform called Driveri, received $16M in Series A from Reliance Industries in India. Two other auto tech startups, Andreessen Horowitz-backed Comma.ai and Oriza Ventures-based Drive.ai, raised $3M and $12M in early-stage funding, respectively. China-based Turing Robot, which was initially focused on voice technologies, is now expanding into the consumer robotics market. It raised $7.6M in corporate minority from Alpha Animation & Culture. Another China-based startup, Rokid, which also has an office in the US, is developing a social robot. You can read more about robotics startups in China in our post here.

BI, Sales & CRM: Applications here include voice analytics to extract information from calls, automated customer response solutions, business data analytics, and sales targeting. To name a few, Palo Alto-based Mariana raised $2M in seed money from investors including Blumberg Capital; London-based True AI, previously seed funded by Entrepreneur First, entered the Microsoft Ventures Accelerator in Q3’16; another UK-based startup, Ripjar, raised funds from Winton Ventures in Q2’16.

Healthcare: As we discussed in our webinar recently, healthcare is the hottest area of investment compared to other industry-specific applications of artificial intelligence. Deep learning startups here include drug discovery platforms Insilico and Atomwise, IBM-backed precision medicine startup Pathway Genomics, and diagnostics companies Butterfly Network and Enlitic.

Security: Israel-based Deep Instinct, which claims to be the first startup to bring deep learning to cybersecurity, is backed by investors including Blumberg Capital, UST Global, and U.S. News & World Report. Other startups here include Seattle-based SignalSense, which applies deep learning to IT security and smart camera startup Umbo CV.

E-Commerce: Deep learning in e-commerce was spotlighted recently by Etsy’s acquisition of Blackbird Technologies. Three startups in the private sector using AI in e-commerce raised funding rounds this year: Reflektion raised $18M in Q1’16 from investors including Intel Capital, Battery Ventures, and Marc Benioff; ViSenze raised $10.5M in Series B from investors including Rakuten Ventures, Enspire Capital, and Phillip Private Equity; India-based Staqu raised angel funds in Q2’16.

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  • tomvandendooren

    Feels like there is at least one Core AI field missing: sensor and context intelligence, applying deep learning to analyze (mobile) sensor data to understand human behavior and real-time context. Ultimately, being able to contextualize and interpret human behavior from observed data will be key to develop intelligent devices, applications and services which conform to and preempt user needs and wants in real-time context.